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A Progressive Fast CU Split Decision Scheme for AVS3

A Progressive Fast CU Split Decision Scheme for AVS3

引用

IEEE visual communications and image processing (VCIP)

作者： YUYUAN CHEN SONGLIN SUN JIAQI ZHANG SHANSHE WANG BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS BEIJING CHINA INSTITUTE OF COMPUTING TECHNOLOGY CHINESE ACADEMY OF SCIENCES BEIJING CHINA NATIONAL ENGINEERING LABORATORY FOR VIDEO TECHNOLOGY PEKING UNIVERSITY BEIJING CHINA

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

AVS3 is the newest video coding standard developed by AVS (Audio Video coding Standard) group. AVS3 adopted QTBT(Quad-tree and Binary-tree) plus EQT(Extended quad-tree) block partition scheme, which makes the split process more flexible. The CU split structure is determined by a brute-force rate-distortion optimization (RDO) search. After the whole RDO search, the CU partition with minimum RD cost is selected. The flexible block partition and thorough RDO search bring promising coding gain while extremely complicate the encoder. To reduce the computational complexity of the CU split decision process in AVS3, this paper proposed a spatial information based fast split decision algorithm. In the proposed algorithm, the predicted value of split complexity was calculated firstly according to the information of spatial neighboring blocks. Then the predicted value was used to decide whether to split current CU or not. The experimental results show that the proposed algorithm resulted in average 31.03% encoding time saving with average 0.54% BD-BR loss for Random Access (RA) configuration. The proposed algorithm can greatly reduce the computational complexity of the CU split decision process with negligible performance loss.

关键词： Encoding Prediction algorithms Partitioning algorithms Standards Correlation Copper image coding

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A New Bounding Box based Pseudo Annotation Generation Method for Semantic Segmentation

A New Bounding Box based Pseudo Annotation Generation Method...

引用

IEEE visual communications and image processing (VCIP)

作者： Xiaolong Xu Fanman Meng Hongliang Li Qingbo Wu King Ngi Ngan Shuai Chen School of ICE University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

This paper proposes a fusion-based method to generate pseudo-annotations from bounding boxes for semantic segmentation. The idea is to first generate diverse foreground masks by multiple bounding box segmentation methods, and then combine these masks to generate pseudo-annotations. Existing methods generate foreground masks from bounding boxes by classical segmentation methods driving by low-level features and own local information, which is hard to generate accurate and diverse results for the fusion. Different from the traditional methods, multiple class-agnostic models are modeled to learn the objectiveness cues by using existing labeled pixel-level annotations and then to fuse. Firstly, the classical Fully Convolutional Network (FCN) that densely predicts the pixels' labels is used. Then, two new sparse prediction based class-agnostic models are proposed, which simplify the segmentation task as sparsely predicting the boundary points through predicting the distance from the bounding box border to the object boundary in Cartesian Coordinate System and the Polar Coordinate System, respectively. Finally, a voting-based strategy is proposed to combine these segmentation results to form better pseudo-annotations. We conduct experiments on PASCAL VOC 2012 dataset. The mIoU of the proposed method is 68.7%, which outperforms the state-of-the-art method by 1.9%.

关键词： Annotations image segmentation Training Semantics Predictive models Task analysis Pipelines

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Adaptive CU Split Decision with Pooling-variable CNN for VVC Intra Encoding 34

Adaptive CU Split Decision with Pooling-variable CNN for VVC...

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Tang, Genwei Jing, Minge Zeng, Xiaoyang Fan, Yibo Fudan University State Key Lab of ASIC System Shanghai200433 China

ISBN: (纸本)9781728137230

In the versatile video coding (VVC) proposed by the Joint Video Exploration Team (JVET), the quad-Tree with the nested multi-Type tree (QTMT) partition scheme has been adopted based on the quadtree structure in the high efficiency video coding (HEVC). The video coding quality of VVC is better than the HEVC, but the algorithm complexity has also increased greatly. In this work, we present an adaptive CU split decision for intra frame with the pooling-variable convolutional neural network (CNN), targeting at various coding unit (CU) shape. The shape-Adaptive CNN is realized by the variable pooling layer size where we can make the most of the pooling layer in CNN and retain the original information. Based on the proposed CNN, the CU split or not will be decided by only one trained network, same architecture and parameters for the CUs with multiple sizes. Moreover, with the proposed shape-based CNN training scheme, the various training sample size can be processed successfully. The CUbased network can avoid the full rate-distortion optimization for the CU split and the CU-level rate control can also be enabled. The experiment results show that the proposed method can save 33% coding time with only 0.99% Bjontegaard Delta bitrate (BD-rate) increase. © 2019 IEEE.

关键词： image coding

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image data augmentation method based on maximum activation point guided erasure

Image data augmentation method based on maximum activation p...

引用

Advances in Computer Technology, Information Science and communications (CTISC), International conference on

作者： Yun Jiang Shengxin Tao Hai Zhang Simin Cao College of Computer Science and Engineering Northwest Normal University Lanzhou 730070 China

ISBN: (数字)9781728165011

ISBN: (纸本)9781728165028

Deep neural networks usually contain tens to hundreds of millions of orders of learning parameters that provide the necessary representation to solve various visual tasks. But with the increase of the representational ability, the possibility of over-fitting also increase, which bring about poor generalization. In this paper, we propose MA (Maximum Activation point processing) algorithm, a new image data augmentation method which is designed to improve the generalization ability of the model and reduce the risk of overfitting. During the training process, the most discriminative part of the input image is searched for, and the model is driven to search for the supplementary information of the most important feature information by erasing the maximum attention image block. During this process, training images with different occlusion levels are generated as new inputs to the network and the model continues to be trained. The image erasure method based on the maximum activation point guidance only needs to modify the input image, which can effectively improve the robustness of the model to occluded image recognition, and can be integrated with various network structures. The effectiveness of our method is verified on the Cifar10, Cifar100 and Fashion-MNIST datasets.

关键词： Training Convolutional neural networks Feature extraction Data models image recognition Robustness

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Selection-Channel-Aware Deep Neural Network to Detect Motion Vector Embedding of HEVC Videos

Selection-Channel-Aware Deep Neural Network to Detect Motion...

引用

IEEE International conference on Signal processing, communications and Computing (ICSPCC)

作者： Xiongbo Huang Yongjian Hu Yufei Wang Beibei Liu Shuowei Liu School of Electronic and Information Engineering South China University of Technology Guangzhou China Sino-Singapore International Joint Research Institute Guangzhou China

ISBN: (数字)9781728172026

ISBN: (纸本)9781728172033

It is well established that using the selection channel, the probabilities with which the elements in cover are modified during message embedding, would improve the performance of steganalysis. Most video steganographical algorithms embed secret messages in the compressed domain by modifying the motion vectors having less impact on video visual quality, which can be considered as a form of selection channel. Recently, deep neural networks have been rapidly developed for multimedia steganalysis. Although there have been some selection-channel-aware networks for image steganalysis, they cannot be simply extended to video steganalysis because there are great differences between image and video steganographic modification. To our best knowledge, there have been no selection-channel-aware networks for video steganalysis in literature. In this article, we propose a selection-channel-aware deep neural network for video steganalysis. Considering that video structure is quite different from that of image, we focus on the construction of input data matrix for deep convolutional neural network, the definition of probability for motion vector modification, and the network structure of using the selection channel knowledge. Experimental results have demonstrated that the proposed method benefits from selection channel and has satisfactory performance on testing HEVC videos.

关键词： Videos Bit rate Neural networks Feature extraction Knowledge engineering image coding Testing

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Data Collection and image processing Tool for Face Recognition 21st

Data Collection and Image Processing Tool for Face Recogniti...

引用

21st International conference on Human-Computer Interaction (HCII)

作者： Maciel, Francimar Rodrigues Tamayo, Sergio Cleger Khurshid, Aasim Caetano Martins, Pauliana Caetano SIDIA Inst Ciencia & Tecnol Manaus Amazonas Brazil

ISBN: (纸本)9783030307127;9783030307110

Many biometric systems are being used to identify transactions and increase security levels. These systems analyze the different registers that can recognize a person, for example, fingerprint, face, voice, and iris. Face recognition systems are widely studied for security, surveillance applications, transaction, and general services. The accuracy of these systems depends mainly on two closely related factors, quality data and machine learning techniques used. In this paper, we present a data collection and image analysis tool for face recognition with evolved parameters (ergonomic and visual) setting. The proposed tool is capable of collecting face data with various poses while making the user interaction intuitive and comfortable. The details of the different stages of study, along with discussions, is presented based on results extracted from 79 users.

关键词： Data collection Face recognition Robust face data collection

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A Robust Watermarking Scheme for Encrypted JPEG Bitstreams with Format-Compliant Encryption 18

A Robust Watermarking Scheme for Encrypted JPEG Bitstreams w...

引用

18th IEEE International conference on Trust, Security and Privacy in Computing and communications (IEEE TrustCom) / 13th IEEE International conference on Big Data Science and Engineering (IEEE BigDataSE)

作者： Chen, Wenhao Zeng, Bohang Zheng, Peijia Sun Yat Sen Univ Sch Data & Comp Sci GuangDong Prov Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China

ISBN: (纸本)9781728127767

JPEG is a very popular image compression standard in Internet. To simultaneously provide image content protection and copyright protection, in this paper, we provide a robust watermarking scheme for encrypted JPEG bitstreams. The JPEG bitstream is enciphered with a format-compliant encryption to produce another JPEG bitstream with meaningless image content. The watermark embedder can directly embed the message into the encrypted JPEG stream by modifying the quantized DC coefficients without performing decryption or decompression in advanced. As for the watermark extraction, anyone who has the embedding key can extract the watermark from the encrypted watermarked JPEG bitstream and the decrypted watermarked JPEG bitstream. All the watermark embedding and extraction procedures are performed in the compressed domain. We conduct the experiments to verify the effectiveness of the proposed JPEG encryption and watermarking algorithms. Our experimental results also show that the decrypted watermarked images have high visual fidelity, and the watermarking scheme is robust against some common watermarking attacks.

关键词： image watermarking multimedia encryption format-compliant encryption robust watermarking signal processing in the encrypted domain

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Weakly Supervised Learning for Blind image Quality Assessment 34

Weakly Supervised Learning for Blind Image Quality Assessmen...

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： He, Weiquan Gao, Xinb Lu, Wen Guan, Ruoxuan Xidian University School of Electronic Engineering Xian710071 China

ISBN: (纸本)9781728137230

The blind image quality assessment (BIQA) metric based on deep neural network (DNN) achieves the best evaluation accuracy at present, and the depth of neural networks plays a crucial role for deep learning-based BIQA metric. However, training a DNN for quality assessment is known to be hard because of the lack of labeled data, and getting quality labels for a large number of images is very time consuming and costly. Therefore, training a deep BIQA metric directly will lead to over-fitting in all likelihood. In order to solve this problem, we introduced a weakly supervised approach for learning a deep BIQA metric. First, we pre-Trained a novel encoder-decoder architecture by using the training data with weak quality annotations. The annotation is the error map between the distorted image and its undistorted version, which can roughly describes the distribution of distortion and can be easily acquired for training. Next, we fine-Tuned the pre-Trained encoder on the quality labeled data set. Moreover, we used the group convolution to reduce the parameters of the proposed metric and further reduce the risk of over-fitting. These training strategies, which reducing the risk of over-fitting, enable us to build a very deep neural network for BIQA to have a better performance. Experimental results showed that the proposed model had the state-of-Art performance for various images with different distortion types. © 2019 IEEE.

关键词： Deep neural networks

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Design and Development of Leukemia Identification System Through Neural Network and SVM Approach for Microscopic Smear image Database 2nd

Design and Development of Leukemia Identification System Thr...

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2nd International conference on Advanced Informatics for Computing Research (ICAICR)

作者： Rege, M., V Gawali, B. W. Gaikwad, S. Dr BAM Univ Dept CS & IT Aurangabad Maharashtra India Model Coll Dept Comp Sci Jalna MS India

ISBN: (纸本)9789811331404;9789811331398

The recognition of blood disorder through the visual observation is the most challenging job. In the current technological era computer become the most important part of medical science. The haematological disorders of white blood cells (WBC) are really frequent in medical practices. The objective of this research is to design and development of automated identification of Leukemia using microscopic blood smear image database. This proposed scheme uses the most significant steps of image processing like, pre-processing, image segmentation, extraction of features and classification. The Leukemia smear image database is segmented using Otsu image segmentation. The feature extraction extracts the area, perimeter, solidity, orientation, eccentricity, centroid, entropy and energy features. The classification method applied using neural network, Support vector machine and QDA approach. In the neural network the 60% dataset has been passed for the training, 35% towards the testing and remaining 05% is used for the validation. The SVM and QDA classify the dataset for the two groups such as normal and leukaemia. The classification is done on the extracted 08 features of each image. The performance of the neural network is achieving 98.97% with 1.0246 error rate. The support vector machine is shows the 99.35% accuracy with 0.6500 error rate. The QDA classification reported the 99.70% accuracy with 0.300% error rate. From the reported accuracy the QDA and support vector machine proved as dominant as the neural network.

关键词： Leukemia Microscopic Smear Otsu SVM QDA Neural network

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Competing Ratio Loss for Multi-class image Classification 34

Competing Ratio Loss for Multi-class image Classification

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34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Zhang, Ke Wang, Xinsheng Guo, Yurong Zhao, Zhenbing Ma, Zhanyu North China Electric Power University Department of Electronic and Communication Engineering Hebei China Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728137230

Cross-entropy loss function (CEL) is widely used for training a multi-class classification deep convolutional neural network (DCNN). While CEL has been successfully implemented in image classification tasks, it only focuses on the posterior probability of correct class when the labels of training images are one-hot. It cannot be discriminated against the classes not belong to correct class (wrong classes) directly. Negative Log Likelihood Ratio Loss (NLLR) is proposed to better discriminate the correct class from competing wrong classes. But optimization of the loss function is normally presented as a minimization problem. In training DCNN, the value of NLLR is not constantly positive or negative, which affects the convergence of NLLR adversely. So, we propose competing ratio loss (CRL), which calculates the posterior probability ratio between the correct class and competing wrong classes to better widen the difference between the probability of the correct class and the probabilities of wrong classes, which also assures the value of CRL is constantly positive. Through massive experiments, we demonstrate the effectiveness and robustness of CRL on deep convolutional neural networks, our CRL outperforms CEL and NLLR on CIFAR-10/100 datasets. © 2019 IEEE.

关键词： image classification

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